Quantum Computer with Improved Quantum Optimization by Exploiting Marginal Data

ABSTRACT

A quantum optimization system and method estimate, on a classical computer and for a quantum state, an expectation value of a Hamiltonian, expressible as a linear combination of observables, based on expectation values of the observables; and transform, on the classical computer, one or both of the Hamiltonian and the quantum state to reduce the expectation value of the Hamiltonian.

BACKGROUND

Quantum computers promise to solve industry-critical problems which areotherwise unsolvable. Key application areas include chemistry andmaterials, bioscience and bioinformatics, logistics, and finance.Interest in quantum computing has recently surged, in part, due to awave of advances in the performance of ready-to-use quantum computers.Although these machines are not yet able to solve useful industryproblems, the precipice of utility seems to be rapidly approaching.

In addition to the improvements in the quantum hardware, recentalgorithmic developments have created new opportunities for achievingquantum utility. In particular, the variational quantum eigensolver(VQE) algorithm and the quantum approximate optimization algorithm(QAOA) do not demand the same degree of quantum resources as do thewell-known Shor's factoring and Grover's search algorithm. These newalgorithms are used to solve the ground-state energy problem and thewell-known, NP-hard combinatorial optimization problem MAXCUT,respectively. Therefore, these algorithms give hope for solving usefulproblems on near-term quantum hardware.

There are a number of limiting factors that must be overcome toimplement useful instances of these algorithms on near-term quantumdevices. First, the device needs to bear a sufficiently large number ofqubits. Otherwise, classical methods for solving the problem willoutperform the quantum computer. Second, the processes, or quantumgates, on the quantum computer need to be of sufficiently high fidelity.Low fidelity quantum gates will output quantum states with degradedcoherence, and coherent output states are necessary for many of theseapplications. The performance of algorithms such as VQE and QAOA can, inprinciple, can be systematically improved by including more and morelayers of quantum gates. The number of layers of gates is referred to asthe circuit depth. By increasing the circuit depth, one increases theexpressibility of the output state. For example, the maximal degree ofquantum entanglement in the output state is related to the depth of thecircuit preparing the state. Preparing a variety of entangled states ina coherent fashion is necessary for the functioning of many algorithms.Therefore, when executing a quantum algorithm such as VQE or QAOA, oneseeks to maximize the expressibility of the circuit, while at the sametime minimizing the decoherence of the output state.

Ideally, a quantum computer would implement perfect gates, so that onewould not need to decrease depth to minimize decoherence. This is not,however, possible in practice.

SUMMARY

A quantum optimization system and method estimate, on a classicalcomputer and for a quantum state, an expectation value of a Hamiltonian,expressible as a linear combination of observables, based on expectationvalues of the observables; and transform, on the classical computer, oneor both of the Hamiltonian and the quantum state to reduce theexpectation value of the Hamiltonian.

Other features and advantages of various aspects and embodiments of thepresent invention will become apparent from the following descriptionand from the claims.

In a first aspect, a quantum optimization method includes estimating, ona classical computer and for a quantum state, an expectation value of aHamiltonian, expressible as a linear combination of observables, basedon expectation values of the observables. The quantum optimizationmethod also includes transforming, on the classical computer, one orboth of the Hamiltonian and the quantum state to reduce the expectationvalue of the Hamiltonian.

In certain embodiments of the first aspect, the method further includesmeasuring the expectation value of each of the observables on a quantumcomputer by generating the quantum state on the quantum computer, andmeasuring, on the quantum computer, each of the observables for thequantum state.

In certain embodiments of the first aspect, generating the quantum stateincludes generating the quantum state with a parametrized quantumcircuit programmable via one or more circuit parameters.

In certain embodiments of the first aspect, the method further includesupdating the one or more circuit parameters such that the parametrizedquantum circuit outputs an updated quantum state that betterapproximates a ground state of the Hamiltonian.

In certain embodiments of the first aspect, the method further includesrepeating (i) generating the quantum state with the parametrized quantumcircuit, (ii) measuring each of the observables for the quantum state,(iii) transforming one or both of the Hamiltonian and the quantum state,(iv) updating the Hamiltonian based on the transforming, and (v)updating the one or more circuit parameters, until the one or morecircuit parameters have converged.

In certain embodiments of the first aspect, transforming one or both ofthe Hamiltonian and the quantum state includes applying a unitarytransformation to said one or both of the Hamiltonian and the quantumstate.

In certain embodiments of the first aspect, the method further includesgenerating, on the classical computer, the expectation value of each ofthe observables.

In certain embodiments of the first aspect, the method further includesupdating, on the classical computer, a first representation of thequantum state based on the expectation value of the Hamiltonian tobetter approximate a ground state of the Hamiltonian.

In certain embodiments of the first aspect, the method further includesrepeating (i) generating the expectation value of each of theobservables, (ii) transforming one or both of the Hamiltonian and thequantum state, and (iii) updating the first representation of thequantum state, until the first representation of the quantum state hasconverged.

In certain embodiments of the first aspect, the linear combination ofthe observables includes at least one observable with a zero weight thatbecomes non-zero due to said transforming the Hamiltonian. Furthermore,the expectation values of the observables include an expectation valuefor the at least one observable with a zero weight.

In certain embodiments of the first aspect, transforming one or both ofthe Hamiltonian and the quantum state includes applying a fermionictransformation to the one or both of the Hamiltonian and the quantumstate.

In certain embodiments of the first aspect, the fermionic transformationincludes rotations of active orbitals.

In certain embodiments of the first aspect, the fermionic transformationincludes transformations out of an active space to incorporate at leastone of a core orbital and a virtual orbital.

In certain embodiments of the first aspect, the fermionic transformationincludes rotations that respect one or more of an open-shell spinsymmetry, a closed-shell spin symmetry, and a geometric symmetry.

In certain embodiments of the first aspect, the method further includesimplementing a quantum subspace expansion technique.

In certain embodiments of the first aspect, the method further includesimplementing a marginal projection technique.

In certain embodiments of the first aspect, the method further includesobtaining any of the expectation values the observables via orbitalframes.

In certain embodiments of the first aspect, transforming one or both ofthe Hamiltonian and the quantum state includes applying a Majoranafermionic transformation to the one or both of the Hamiltonian and thequantum state.

In certain embodiments of the first aspect, the method further includesminimizing the expectation value of the Hamiltonian using a Givensparameterization.

In certain embodiments of the first aspect, the method further includesminimizing the expectation value of the Hamiltonian using semidefiniteprogramming.

In certain embodiments of the first aspect, transforming one or both ofthe Hamiltonian and the quantum state includes applying a spintransformation to the one or both of the Hamiltonian and the quantumstate.

In certain embodiments of the first aspect, the method further includesminimizing the expectation value of the Hamiltonian using semidefiniteprogramming.

In certain embodiments of the first aspect, the Hamiltonian is an IsingHamiltonian configured for solving a combinatorial optimization problem.

In certain embodiments of the first aspect, the method further includesminimizing the expectation value of the Hamiltonian using semidefiniteprogramming.

In certain embodiments of the first aspect, transforming one or both ofthe Hamiltonian and the quantum state includes minimizing theexpectation value of the Hamiltonian estimated for the quantum state.

In certain embodiments of the first aspect, minimizing the expectationvalue of the Hamiltonian includes minimizing the expectation value ofthe Hamiltonian using semidefinite programming.

In a second aspect, a computer system configured for quantumoptimization includes a processor and a memory communicably coupled withthe processor and storing machine-readable instructions. Wen executed bythe processor, the machine-readable instructions control the computingsystem to estimate, for a quantum state, an expectation value of aHamiltonian, expressible as a linear combination of observables, basedon expectation values of the observables. The machine-readableinstructions also control the computing system to transform one or bothof the Hamiltonian and the quantum state to reduce the expectation valueof the Hamiltonian estimated for the quantum state.

In certain embodiments of the second aspect, the computing systemincludes a quantum computer that is communicably coupled with theprocessor and configured to measure the expectation value of each of theobservables.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a diagram of a quantum computer according to one embodiment ofthe present invention;

FIG. 2A is a flowchart of a method performed by the quantum computer ofFIG. 1 according to one embodiment of the present invention;

FIG. 2B is a diagram of a hybrid quantum-classical computer whichperforms quantum annealing according to one embodiment of the presentinvention;

FIG. 3 is a diagram of a hybrid quantum-classical computer according toone embodiment of the present invention; and

FIG. 4 is a flow chart of a quantum optimization method, in embodiments.

DETAILED DESCRIPTION

Embodiments of the present invention are directed to a quantum computerwhich effectively appends quantum gates to a variational quantum circuitto artificially extend the depth of the circuit. When the quantumcomputer executes variational quantum algorithms, the resultingestimates that are output by the quantum computer are of higher qualitythan the estimates produced by quantum computers without the appendedquantum gates.

Embodiments of the present invention extend the capabilities of quantumcomputers for solving problems such as, but not limited to, ground stateenergy calculations and combinatorial optimization. Quantum computersimplemented according to embodiments of the present invention requirelittle to no additional quantum computational overhead compared toexisting quantum computers because the same effect as appending quantumgates, or increasing circuit depth, is achieved by postprocessing dataon a classical computer, without adding any actual quantum gates to thequantum computer.

Embodiments of the present invention may include performing somecomputations using a classical computer and other computations using aquantum computer. The computations (e.g., optimization routines)executed by the classical computer may be performed in parallel with thecomputations performed by the quantum computer. In general, theclassical and quantum processors may work in tandem to provide anapproximately optimal answer. In general, the role of the quantumcomputer is to generate statistically sampled bit strings, and the roleof the classical processor is to analyze these sampled bit strings andto adapt the quantum processor accordingly.

Embodiments of the present invention use quantum computation data thatis typically not used, to improve the utility of the quantum computer.In particular, typically when a quantum computer executes a variationalquantum algorithm to produce a plurality of output values (e.g., Pauliproduct expectation value estimates), only a single fixed linearcombination of such values is typically used. In contrast, embodimentsof the present invention use a plurality of linear combinations of theindividual output values, not merely the fixed linear combination ofthose values, which results in a better estimate of the optimal valuebeing targeted. Quantum computers implemented according to embodimentsof the present invention exploit algebraic structure in the probleminstance (e.g., the quantum Hamiltonian) to process this data and tooutput a lower estimate of the ground state energy.

As stated above, ideally a quantum computer would implement perfectgates, so that one would not need to decrease circuit depth to minimizedecoherence. This is not, however, possible in practice, because anincreased circuit depth results in increased decoherence. Embodiments ofthe present invention implement a compromise by using additionalclassical processing to achieve the same effect as appending idealquantum gates to the end of the sequence of imperfect quantum gatesimplemented on the quantum computer. This improves the expressibility ofthe parameterized quantum circuit, while not detracting from thecoherence of the output state. More specifically, in embodiments of thepresent invention, a quantum computer, having imperfect gates,repeatedly executes a quantum circuit followed by quantum measurement toproduce initial output data. A classical computer then processes theinitial output data. This classical post-processing effectively appendsa sequence of perfect quantum gates to the imperfect gates of thequantum computer. The term “expressibility” refers to the ability of thequantum computer to generate a variety of highly coherent entangledquantum states as the output of the quantum circuit.

Embodiments of the present invention can be used to extend thecapabilities of quantum computers when implementing variational quantumalgorithms such as VQE or QAOA by producing more accurate results and/ortackling larger problem instances. Such quantum algorithms are used toefficiently generate good approximate solutions to ground state energyproblems and combinatorial optimization problems, which have applicationto drug and materials discovery, as well as route optimization andartificial intelligence.

Some of the embodiments described herein generate, measure, or utilizequantum states that approximate a target quantum state (e.g., a groundstate of a Hamiltonian). As will be appreciated by those skilled in theart, there are many ways to quantify how well a first quantum state“approximates” a second quantum state. In this description, any conceptor definition of approximation known in the art may be used withoutdeparting from the scope hereof. For example, when the first and secondquantum states are represented as first and second vectors,respectively, the first quantum state approximates the second quantumstate when an inner product between the first and second vectors (calledthe “fidelity” between the two quantum states) is greater than apredefined amount (typically labeled E). In this example, the fidelityquantifies how “close” or “similar” the first and second quantum statesare to each other. The fidelity represents a probability that ameasurement of the first quantum state will give the same result as ifthe measurement were performed on the second quantum state. Proximitybetween quantum states can also be quantified with a distance measure,such as a Euclidean norm, a Hamming distance, or another type of normknown in the art. Proximity between quantum states can also be definedin computational terms. For example, the first quantum stateapproximates the second quantum state when a polynomial time-sampling ofthe first quantum state gives some desired information or property thatit shares with the second quantum state.

In the prior art variational quantum algorithm, the problem Hamiltonianmay be defined and mapped to a sum of Pauli product terms. The quantumstate may then be prepared, and the expectation value of each Pauli termmay be measured on the quantum computer. The energy expectation valuemay then be estimated, and a classical optimization routine may be usedto suggest new state preparation parameters based on the energyexpectation value estimate. The algorithm may then return to thebeginning and prepare the updated quantum state based on the updatedcircuit parameters.

A quantum computer implemented according to an embodiment of the presentinvention (which may operate under the control of a classical computer)may also begin by defining the problem Hamiltonian and mapping theproblem Hamiltonian to a sum of Pauli product terms. A quantum computerimplemented according to an embodiment of the present invention mayprepare the quantum state and measure the expectation value of eachPauli term. The quantum computer implemented according to an embodimentof the present invention, or a classical computer which receives outputof the previous steps from the quantum computer, may then perform amarginals optimization procedure (MOP), such as by optimizing energy andupdating the Hamiltonian. The classical computer may then optimize theenergy in any of a variety of ways by exploiting structure in themarginal expectation values to carry out additional minimization stepstowards obtaining the minimum energy. The outputs of this energyoptimization are the optimized energy value and the optimaltransformation, both of which may be used to update the Hamiltonian.

Once the energy has been optimized and the Hamiltonian has been updated,the quantum computer implemented according to an embodiment of thepresent invention may update the circuit parameters based on theestimated energy of the updated Hamiltonian, as is typically done in,for example, the variational quantum eigensolver algorithm, and thenreturn to step 1 and prepare the updated quantum state based on theupdated circuit parameters. The quantum computer implemented accordingto an embodiment of the present invention may repeat the process justdescribed any number of times.

A general method of the marginals optimization procedure will now bedescribed in more detail. The marginals optimization procedure makes useof quantum marginal data during the course of a variational quantumalgorithm to accelerate the optimization of the algorithm. Suchalgorithms are examples of hybrid quantum-classical algorithms, in whicha quantum processor and classical processor work in tandem to execute analgorithm. Towards this end, the method introduced herein aims to rampup the effort of the classical processor so as to extract as muchutility from the quantum computer's data output as possible.

Variational quantum algorithms such as VQE and QAOA work as follows:

-   -   1. The problem Hamiltonian is defined and mapped to a sum of        Pauli product terms H=Σ_(i)h_(i)P_(i) on N qubits, where        P_(i)=σ₁ ^(μ) ^(i,1) {circle around (×)} . . . {circle around        (×)} σ_(N) ^(μ) ^(i,N) .    -   2. A variational quantum circuit U_(T)(θ_(T)) . . . U₁(θ₁)        prepares quantum states, |ψ({right arrow over (θ)})        =U_(T)(θ_(T)) . . . U₁(θ₁)|0        ^({circumflex over (×)}N).    -   3. The expectation value of each Pauli term,        P_(i)        , is measured on the quantum computer, with respect to the        prepared state |ψ({right arrow over (θ)})        . This is achieved by repeatedly preparing |ψ({right arrow over        (θ)})        and measuring it in the Pauli basis of P_(i) to record a binary        outcome ±1 in each shot.    -   4. The energy expectation value is estimated as        H        =Σ_(i) h_(i)        P_(i)        .    -   5. A classical optimization routine is used to suggest new state        preparation parameters based on the energy expectation value        estimate. (Note: depending on the classical optimization        routine, multiple loss function evaluations may be executed        before new circuit parameters are suggested.)    -   6. Return to Step 1, preparing the updated state.

MOP may, for example, replace Step 3 above as follows. An observation ofMOP is that there is structure in the marginal expectation values

P_(i)

which may be exploited to carry out an additional minimization steptowards obtaining the minimum energy. Generally, depending on thestructure of the Hamiltonian, there may be a group of unitary operationsU_(R) which preserves the space of marginals:

${U_{R}^{\dagger}P_{i}U_{R}} = {\sum\limits_{j}{R_{i}^{j}{P_{j}.}}}$

This enables a free depth increase of the variational quantum circuit,boosting the opportunity to drive the state preparation towards theground state. By carrying out the following optimization routine, MOPmay search about a manifold of quantum states to find a betterapproximation to the ground state energy in each loop of the variationalquantum algorithm:

$\min\limits_{R}{\sum\limits_{i}{h_{i}R_{i}^{j}{\langle P_{j}\rangle}}}$

subject to “certain conditions on R”

There are several possible ways to carry out this optimization routine.We will describe examples of concrete methods for carrying out theoptimization for each of the example applications.

The outputs of this marginals optimization procedure are the optimizedenergy value and the optimal transformation R*. It is useful to viewthis transformation as a Heisenberg transformation of the targetHamiltonian:

${{H_{n}->{H_{n + 1} \equiv {\sum\limits_{i,j}{h_{i}^{(n)}R_{i}^{j}P_{j}}}}} = {\sum\limits_{j}{h_{j}^{({n + 1})}P_{j}}}},$

where n indexes the Hamiltonian coefficients in the nth loop of thevariational quantum algorithm. Such a transformation does not change thespectrum of the Hamiltonian, and thus the ground state energy of H_(n)is equal to the ground state energy of H. The value of transforming theHamiltonian is that the variational circuit may be better able toprepare an approximation to the ground state of H_(n) than to the groundstate of the initial Hamiltonian H.

The MOP-enhanced variational quantum algorithm may then work as follows:

-   -   1. The problem Hamiltonian is defined and mapped to a sum of        Pauli product terms H₀=Σ_(i)h_(i)P_(i) on N qubits, where        P_(i)=σ₁ ^(μ) ^(i,1) {circle around (×)} . . . {circle around        (×)} σ_(N) ^(μ) ^(i,N) .    -   2. A variational quantum circuit U_(T)(θ_(T)) . . . U₁(θ₁)        prepares quantum states, |ψ({right arrow over (θ)})        =U_(T)(θ_(T)) . . . U₁(θ₁)|0        ^({circle around (×)}N).    -   3. The expectation value of each Pauli term,        P_(i)        , is measured on the quantum computer, with respect to the        prepared state |ψ({right arrow over (θ)})        . This is achieved by repeatedly preparing |ψ({circle around        (θ)})        and measuring it in the Pauli basis of P_(i) to record a binary        outcome ±1 in each shot.    -   4. The classical optimization routine

$\min\limits_{R}{\sum\limits_{i,j}{h_{j}R_{i}^{j}{\langle P_{i}\rangle}}}$

is carried out, which outputs the updated energy expectation valueΣ_(i,j)h_(j) [R*]_(i) ^(j)

(P_(i)

and updated Hamiltonian H_(n)→H_(n+1)=Σ_(i,j)h_(j) [R*]_(i) ^(j)P_(i),where R* is the optimal transformation which acts unitarily on theHamiltonian.

-   -   5. An additional classical optimization routine is used to        suggest new state preparation parameters based on the energy        expectation value estimate. (Note: depending on the classical        optimization routine, multiple loss function evaluations may be        executed before new circuit parameters are suggested.)    -   6. Return to Step 1, preparing the updated state.

More generally, it may be the case that a finite number of additionalmarginals Q_(j) should be added in order that the space of marginals,now {P_(i),Q_(j)}, is preserved by U_(R). When the size of the set{P_(i),Q_(j)} grows no more than polynomially in the number of qubits,for a given application, MOP remains computationally efficient.

It is valuable to view MOP in light of the N-representability problem,or, more generally, the quantum marginal problem. Literature on theseproblems highlights the fact that the energy expectation value of ak-body Hamiltonian on N qubits is a function of at most

$O\left( {\begin{pmatrix}N \\K\end{pmatrix}4^{k}} \right)$

parameters, as opposed to the O(4^(N)) needed to describe the state.Furthermore, the space of

$O\left( {\begin{pmatrix}N \\K\end{pmatrix}4^{k}} \right)$

parameters forms a convex set. At first glance, it seems, then, that theproblem of energy minimization should be efficiently solvable bycarrying out a convex optimization in this few-parameter space. Thecatch, however, is the fact that the convex set of valid parametervalues does not admit a tractable characterization. Moreover, theN-representability problem and quantum marginal problem have been shownto be QMA-complete.

Through MOP, we gain a more general perspective on the role of thequantum computer in variational quantum algorithms. The quantum computersupplies valid quantum marginal data, while the classical processorcalculates energy expectation values and solicits new marginal datathrough new state preparations. While the standard approach to VQEcarries out the optimization based only on the energy expectation value,MOP carries out an additional optimization exploiting the availablevalid quantum marginal data. Thus, rather than just energy expectationvalues, the commodity supplied by the quantum computer appears to be thevalid quantum marginal data.

We describe MOP as applied, for example, to the case of a Fermionictwo-body Hamiltonian. Consider the general fermionic Hamiltonian withCoulomb repulsion in second-quantization form,

${H = {{\sum\limits_{i,j}{S^{ij}a_{i}^{\dagger}a_{j}}} + {\sum\limits_{i,j,k,l}{D^{ijkl}a_{i}^{\dagger}a_{j}^{\dagger}a_{k}a_{l}}}}},$

and where the S_(ij) account for a “number penalty” term to force theoutput to be of the proper particle number. When implementing thestandard variational quantum eigensolver, the algorithm concludes withan optimized state preparation using a variational circuit outputtingthe state ρ({right arrow over (θ)}*), for which the energy is estimated,up to sampling error, according to

E({right arrow over (θ)}*)

=Tr(Hρ({right arrow over (θ)}*)),

where ρ({right arrow over (θ)}*) accounts for implementation and readouterror. In the final round of VQE, the expectation value of, in general,each term V_(ij)=Tr(a_(i) ^(†)a_(j)ρ({right arrow over (θ)}*))) andW_(ijkl)=Tr(a_(i) ^(†)a_(j) ^(†)a_(k)a_(l)ρ({right arrow over (θ)}*))has been computed. These values are known as the 1- and 2-particlereduced density matrices (RDMs). We show how, with just classicalpost-processing, these RDMs can be used to improve the ground stateenergy estimation.

First, consider the case that a sequence of error-free, tunable gatesU_({right arrow over (λ)}) could be applied at the end of thevariational circuit, producing ρ({right arrow over (λ)}, {right arrowover (θ)}*)═U({right arrow over (λ)})ρ({right arrow over (θ)})U({rightarrow over (λ)})^(†). Then, minimizing the energy with respect to tuningthe parameters {right arrow over (λ)} would, in the worst case, maintainthe energy expectation estimation, while in many cases it would lowerit. Of course, in practice, introducing such gates is costly because thecoherence of the output state is compromised by the additional errorfrom these gates. However, we can achieve the effect of implementingthese gates by a particular post-processing technique that we call themarginals optimization procedure (MOP).

Consider re-expressing the energy expectation with these additionalgates in the Heisenberg picture,

H

=Tr(HU({right arrow over (λ)})ρ({right arrow over (θ)})U({right arrowover (λ)})^(†))=Tr(U({right arrow over (λ)})^(†) HU({right arrow over(λ)})ρ({right arrow over (θ)})).

In the example of the fermionic Hamiltonian, if we choose the gatesU({right arrow over (λ)}) to correspond to fermionic orbital rotations,then their action on the creation or annihilation operators of theHamiltonian is to output a linear combination of creation orannihilation operators, respectively,

${{\langle H\rangle} = {{{\sum\limits_{i,j}{S^{ij}{{Tr}\left( {U_{R}^{\dagger}a_{i}^{\dagger}a_{j}U_{R}{\rho\left( \overset{->}{\theta} \right)}} \right)}}} + {\sum\limits_{i,j,k,l}{D^{ijkl}{{Tr}\left( {U_{R}^{\dagger}a_{i}^{\dagger}a_{j}^{\dagger}a_{k}a_{l}U_{R}{\rho\left( \overset{->}{\theta} \right)}} \right)}}}} = {{S^{ij}R_{i}^{I}*R_{j}^{J}*{{Tr}\left( {a_{I}^{\dagger}a_{J}{\rho\left( \overset{->}{\theta} \right)}} \right)}} + {D^{ijkl}R_{i}^{I}*R_{j}^{J}*R_{k}^{K}R_{l}^{L}{{Tr}\left( {a_{I}^{\dagger}a_{j}^{\dagger}a_{K}a_{L}{\rho\left( \overset{->}{\theta} \right)}} \right)}}}}},$

where the summation is inferred by Einstein notation and the R tensorsare orbital rotation matrices characterized by an element of SU(N),where N is the number of spin orbitals. Noticing that the traces aresimply the 1-RDMs V and 2-RDMs W of ρ({right arrow over (θ)}), asestimated on the quantum computer, we find that we can efficiently (inthe number of spin orbitals) compute the expected energy of the statesU_(R)ρ({right arrow over (θ)})U_(R) ^(†),

H

=Tr(SRVR ^(†))+Tr(D(R{circle around (×)}R)W(R ^(†) {circle around (×)}R^(†))),

where we have reshaped S,V and D,W into N-by-N and N²-by-N² matrices,respectively. Finally, by carrying out the following optimizationproblem we can, in general, obtain an improved estimate of the groundstate energy,

${{\min\limits_{R}{{Tr}\left( {SRVR}^{\dagger} \right)}} + {{Tr}\left( {{D\left( {R \otimes R} \right)}{W\left( {R^{\dagger} \otimes R^{\dagger}} \right)}} \right)}}\mspace{14mu}$subject  to  RR^(†) = I  and  R^(†)R = I

where the two conditions on R ensure unitarity. The energy expectationvalue obtained from this optimization is ensured to be at most thatestimated in the initial VQE experiment. As motivated by theorbital-optimized coupled cluster method and the Breuckner coupledcluster method such orbital rotations stand to improve the energyestimates in practice. The minimization problem above can be approachedin a number of ways. While black-box optimization may be used, it may beuseful to employ a method which exploits the structure in theoptimization problem. We emphasize that the marginals optimizationprocedure is independent of the particular optimization routine chosen,and we will discuss alternative optimization routines later on.

Finally, we note that, for the Fermionic case, the marginalsoptimization procedure can be implemented in both a restricted andextended regime. We will discuss these variants in more detail later on.In the first case, it may be that the Hamiltonian does not depend oncertain marginals. This may happen, for example, if the Hamiltonianpossesses certain symmetries. In such cases, it may be useful toconsider just a subgroup of all possible orbital rotations. Indeed, itis possible to restrict the orbital optimization of the equation aboveto just a subgroup of orbital optimizations. An explicit example of thiswould be the case where the Hamiltonian possessed spin-symmetry. Then,one may want to restrict the optimization to rotations of just thespatial orbitals. For the second case, consider that the initialHamiltonian accounted only for a subspace of active orbitals. Followinga VQE calculation one may want to extend the orbital rotations in orderto involve orbitals from the inactive space. Such restrictions andextensions are analogous to standard techniques in quantum chemistry.

The application of the marginals optimization procedure to VQE with aunitary coupled-cluster (UCC) ansatz may be viewed as a novel adaptionof the classical orbital-optimized coupled cluster (OCC) and Breucknercoupled cluster (BCC) algorithms to quantum computers. The OCC and BCCapproaches are based on an ansatz comprised of an orbital rotation and acoupled cluster operator which are simultaneously optimized using aclassical computer.

The notion of restricting the orbital rotation to have the same effecton spin-up and spin-down orbitals is analogous to the treatment of spinin other methods based on orbital rotations, such as Hartree-Fock theoryand Kohn-Sham density-functional theory. MOP without the spinrestriction is analogous to unrestricted Hartree-Fock (UHF), while MOPwith spin restrictions is analogous to restricted Hartree-Fock (RHF) inthe case of closed-shell systems and restricted open-shell Hartree-Fock(ROHF) in the case of open-shell systems.

The general marginal optimization procedure includes, but is not limitedto, using Hamiltonians decomposed into a linear combination of Paulistrings. The only requisite is that estimates for certain quantummarginal data be obtained. An example of using the marginalsoptimization procedure where the Hamiltonian is not decomposed intoPauli strings is as follows. The orbital frames method [Motta, Mario, etal. “Low rank representations for quantum simulation of electronicstructure.” arXiv preprint arXiv:1808.02625 (2018)] provides adecomposition of the Hamiltonian in terms of products of fermionicnumber operators, each rotated by an orbital transformation:

${H = {\sum\limits_{ = 1}^{L}\left\lbrack {{\sum\limits_{i_{1}}^{N}{g_{i_{1}}^{({,1})}n_{i_{1}}^{()}}} + {\sum\limits_{i_{1},{i_{2} = 1}}^{N}{g_{i_{1}i_{2}}^{({,2})}n_{i_{1}}^{()}n_{i_{2}}^{()}}} + {\sum\limits_{i_{1},i_{2},{i_{3} = 1}}^{N}{g_{i_{1}i_{2}i_{3}}^{({,3})}n_{i_{1}}^{()}n_{i_{2}}^{()}n_{i_{3}}^{()}}} + {\bullet {\sum\limits_{i_{1},i_{2},{{\bullet \; i_{k}} = 1}}^{N}{g_{i_{1},{i_{2}\bullet}}^{({,3})}n_{i_{1}}^{()}n_{i_{2}}^{()}{\bullet n}_{K}^{()}}}}} \right\rbrack}},$

where

-   -   g_(i) ^((l,k)) represent coefficients for the k-body terms        obtained from a decomposition of the Hamiltonian,    -   L is the number of frames in the decomposition,    -   K is the highest order term appearing in the Hamiltonian and is        equal to 2 when the Hamiltonian is the electronic structure        Hamiltonian,    -   i_(m) indexes the spin orbitals, and    -   the number operators are

n _(i) ^((l)) =a _(i) ^((l)†) a _(i) ^((l))   (1)

with a_(i) ^((l)*) and a_(i) ^((l)) being the creation and annihilationoperators corresponding to the single-particle orbital

$\psi_{i}^{()} = {\sum\limits_{j}{U_{ji}^{()}\varphi_{j}}}$

where U^((l)) is an N×N matrix obtained from the decomposition,

-   -   l is the index of single-particle orbital bases {ψ_(i) ^((l))}        (where i runs from 1 to N) obtained by the decomposition, and by        which each value of l indexes a different, so-called, orbital        frame.

For a k-body Hamiltonian decomposed in terms of orbital frames, thefermionic marginals up to k-body may be determined by the expectationvalues of the

n_(i₁)^(())  …  n_(i_(m))^(()).

Specifically, the fermionic marginals

?a_(i₁)  …  a_(i_(m))?

may be reconstructed as appropriate linear combinations of the estimatedexpectation values

? n_(i₁)^(())  …  n_(i_(m))^(())?.

With the reconstructed fermionic marginals, the marginals optimizationprocedure may be performed.

To carry out MOP, the transformation on the marginals must beparameterized. One method for parameterizing a unitary or an orthogonalmatrix is with an exponential generator. In the unitary case, anyanti-Hermitian matrix A generates a corresponding unitary U_(A) via thematrix exponential U_(A)=exp(A). Similarly, for the real orthogonaltransformation case using in the Majorana fermionic transformations, anyreal anti-symmetric matrix B generates a corresponding orthogonal matrixO_(B)=exp(B). The coordinates of these matrices A and B constitute theparameters that are varied in a black box optimization used to carry outthe marginals optimization.

In carrying out the optimization subroutine of the marginalsoptimization procedure, it may be beneficial to choose aparameterization of the rotation matrix R which yields analyticgradients. One way to achieve this in the case of fermionic or Majoranafermionic orbital rotations is to employ a, so-called, Givensdecompositions of the rotation matrix. Givens decompositions are closelyrelated to “match gate circuits” which are able to be efficientlysimulated and give a decomposition of Majorana fermionic orbitaltransformations. The Givens decomposition of the orbital transformationgives a parameterization of the rotation where the gradient of theenergy with respect to these parameters is efficiently computable.

The marginals optimization procedure may be used in conjunction with anumber of existing techniques for improving the performance ofvariational quantum algorithms. One example is the quantum subspaceexpansion technique introduced in [McClean, Jarrod R., et al. “Hybridquantum-classical hierarchy for mitigation of decoherence anddetermination of excited states.” Physical Review A 95 (2017): 042308].In the quantum subspace expansion, a set of operators {O_(i)} is used togenerate a subspace of states spanned by {O_(i)ρO_(j) ^(†)}. Theexpectation values H_(ij)=Tr(ĤO_(i)ρO_(j) ^(†)) andS_(ij)=Tr(O_(i)ρO_(i) ^(†)) are measured and then used to solve thegeneralized eigenvalue problem Hv=λSv. The minimum eigenvalue gives theminimum energy of the Hamiltonian Ĥ in the subspace. This technique hasbeen shown to mitigate error and to improve the performance ofoptimization. The marginals optimization procedure may be used inconjunction with quantum subspace expansion as follows. After carryingout a round of marginals optimization to produce an updated HamiltonianH→H′, quantum subspace expansion may be performed with respect to theupdated Hamiltonian.

The marginal estimates obtained during a variational quantum algorithmwill incur a degree of error due to statistical sampling error anddevice error. This can lead to the set of estimated marginals beinginvalid, as defined by the well-known quantum marginals problem or, inthe fermionic case, the N-representability problem. The marginalsprojection technique introduced in [Rubin, Nicholas C., Ryan Babbush,and Jarrod McClean. “Application of fermionic marginal constraints tohybrid quantum algorithms.” New Journal of Physics 20 (2018): 053020]gives a method for adjusting the estimated marginal values to bring themcloser to the set of valid marginals in the case of fermionic probleminstances. This technique may be used in conjunction with the marginalsoptimization procedure by first applying the marginals projectiontechnique to obtain a less-errored set of marginal estimates and thenusing these improved marginal estimates as input to the marginalsoptimization procedure.

MOP may be used in a number of different applications. The choice ofunitary transformations that are optimized over depend on the structureof the Hamiltonian and may vary from problem to problem. We describethree general classes of transformations: orbital rotations, Majoranarotations, and local unitary transformations. For each class we discussexample applications.

In quantum chemistry and materials science, a basic subroutine is thedetermination of the ground state energy. The cost of running classicalalgorithms for accurately estimating the ground state energy of amolecule or material grows exponentially in the number of electronorbitals considered. Several quantum algorithms have been proposed forestimating ground state energies. Recently, the variational quantumeigensolver has attracted significant attention from the quantumcomputing community because of its prospects for implementation onnear-term devices.

In many quantum chemistry and materials settings, the system of interestis described by the general fermionic two-body Hamiltonian as previouslydescribed. During the course of a VQE implementation, which attempts todetermine the ground state energy of this Hamiltonian, the Hamiltoniancan be adapted so as to systematically lower the ground state energyestimates. During the marginals optimization procedure, the followingoptimization problem may be solved:

${\min\limits_{R}\; {{Tr}\left( {SRVR}^{\dagger} \right)}} + {{Tr}\left( {{D\left( {R \otimes R} \right)}{W\left( {R^{\dagger} \otimes R^{\dagger}} \right)}} \right)}$subject  to  RR^(†) = I  and  R^(†)R = I

Blackbox non-linear programming techniques, such as MATLAB's built-infmincon function or the scipy-optimize optimization module can be usedto carry out this optimization. We describe a moretheoretically-motivated alternative with performance guarantees that isbased on semidefinite programming relaxation methods.

In typical quantum chemistry problems, many of the two-body fermionicHamiltonian coefficients, or integrals, will be zero. For example, ifa_(i) and a_(j) correspond to states with different spin, then S^(ij)will be zero and the corresponding marginal V_(ij) need not be measured.Restricting R to be block diagonal, with one block corresponding to spinup orbitals and another block corresponding to spin down orbitals, willprevent the rotation from affecting terms in the Hamiltonian that arezero due to spin considerations.

In some cases, it may be advantageous to consider only orbital rotationsthat have the same effect on both spin-up and spin-down orbitals (i.e.,the two blocks of R are identical). For example, such a restrictioncould be useful for constraining the variational minimization toclosed-shell wavefunctions (i.e., states that are invariant with respectto spin rotations). Provided that the state ρ({right arrow over (θ)}*)is a closed-shell state and that the spatial orbitals corresponding tothe spin-up and spin-down spin orbitals are identical, restricting therotation to have the same effect on spin-up and spin-down orbitalsensures that the transformed state is closed-shell and reduces thenumber of parameters that must be optimized via MOP. It may also beadvantageous to restrict the blocks of R to be identical for someopen-shell cases. For example, restricting R may be required to ensurethat the transformed state is an eigenstate of the total spin operator.Such constraints manifest as homogeneous linear constraints on R, whichwill translate into homogeneous linear constraints on R{circle around(×)}R^(†) that can be easily incorporated into the semidefiniteprogramming method as described below.

We introduce an optimization method which is tailored to the fermionicHamiltonian. We adapt the previous optimization problem into amore-tractable form. First, we reshape the unitaries R into N²dimensional vectors |R

, allowing us to rewrite the minimization problem as

${\min\limits_{R\rangle}{\langle\left. R \middle| {V \otimes S^{T}} \middle| R \right.\rangle}} + {\langle\left. {RR} \middle| {\left( {I \otimes X_{23} \otimes I} \right){W \otimes {D^{T}\left( {I \otimes X_{23} \otimes I} \right)}}} \middle| {RR} \right.\rangle}$subject  to⟨R|(I ⊗ i⟩⟨j)R⟩ = δ_(ij)  and  ⟨R|(i⟩⟨j⊗I)|R⟩ = δ_(ij)

where X₂₃ is the swap operator on the second and third systems. Next,noting that

R|V{circle around (×)}S^(T)|R

=

RR|V{circle around (×)}S^(T){circle around (×)}I{circle around (×)}I|RR

, and defining

M=V{circle around (×)}S ^(T) {circle around (×)}I{circle around(×)}I+(ity{circle around (×)}X ₂₃ {circle around (×)}I)W{circle around(×)}D ^(T)(I{circle around (×)}X ₂₃ {circle around (×)}I),

we simplify the optimization problem to

$\min\limits_{R\rangle}{\langle\left. {RR} \middle| M \middle| {RR} \right.\rangle}$subject  to⟨R|(I ⊗ |i⟩⟨j|)|R⟩ = δ_(ij)  and  ⟨R|(i⟩⟨j|⊗I)R⟩ = δ_(ij)

We introduce the self-consistency approach, where we iteratively solverestricted versions of the above optimization problem. To summarize, wefix one of the rotation matrices, optimize with respect to the other,and then, in the next iteration, set the fixed rotation matrix to theoptimal rotation matrix of the previous iteration. In this approach, theoptimization of each iteration may be carried out using the followingsemidefinite relaxation technique. Defining M_(i)═(I{circle around (×)}

R_(i)|)M(I{circle around (×)}|R_(i)

), where R_(i) is the fixed rotation, the optimization problem becomes,

$\min\limits_{R\rangle}{\langle\left. R \middle| M_{i} \middle| R \right.\rangle}$subject  to  ⟨R|(I ⊗ |i⟩⟨j|)|R⟩ = δ_(ij)  and⟨R|(|i⟩⟨j|⊗I)|R⟩ = δ_(ij)

The above minimization problem has the form of aquadratically-constrained quadratic programming problem. Such problemsare, in general, NP-hard. A standard technique for obtaining approximatesolutions is known as semidefinite relaxation. We propose a type ofsemidefinite relaxation method for generating approximate solutions tothe above optimization problem. The first step is to express theoptimization problem as nearly a semidefinite programming problem:

$\min\limits_{\rho}{{Tr}\left( {M_{i}\rho} \right)}$subject  to  Tr((I ⊗ |i⟩⟨j|)ρ) = δ_(ij  )andTr((|i⟩⟨j|⊗I)ρ) = δ_(ij) ρ  is  rank − one

The problem is not a semidefinite program, however, because of thenon-convex rank-one constraint. The semidefinite relaxation method thenrelaxes the rank-one constraint and solves the resulting semidefiniteprogram, which only takes polynomial-time in the dimension of ρ. Theoptimal value ρ* is then used to generate rank-one candidate solutions,of which the optimal one is taken as the approximate solution. Thecandidates |K

are obtained by sampling from the multivariate normal distribution

(0,ρ*), where ρ* serves as the covariance matrix. The sampled vectors |K

do not necessarily satisfy the quadratic constraints of unitarity givenin the minimization problems above, and are likely infeasible. Togenerate feasible candidates these samples must be rounded, which is astandard technique in semidefinite relaxation methods. We choose toround each sample |K

to the closest unitary in distance given by the Frobenius norm. IfK=USV^(†) is the singular value decomposition of K, the closest unitaryto K is given by W=UV^(†). Thus, we round |K

by vectorizing to obtain matrix K, computing the singular valuedecomposition K=USV^(†), and then taking the rounded R*=UV_(†) to be thecandidate rotation. By generating many samples and choosing the bestone, we boost the chances of generating a good candidate rotation. Fromthe best sample, we obtain our approximate solution to the optimizationproblem and plug this in to the fixed rotation to the next round ofiteration: R_(i+1)=R*. This procedure is continued until the improvementin energy from one round to the next falls below some threshold.

We also introduce a direct semidefinite relaxation approach. For anyfermionic Hamiltonian, we may implement the marginal optimizationprocedure using a more general group of unitary transformations thanjust orbital rotations. This group is known as the Bogoliubovtransformations. Such transformations map linear combinations offermionic creation to linear combinations of creation and annihilationoperators. Bogoliubov transformations have a simple characterization interms of Majorana operators γ_(2i)=(a_(i)+a_(i) ^(†))/t2 andγ_(2i+1)=(a_(i)−a_(i) ^(†))/√{square root over (2)}i. These operatorssatisfy the elegant, single commutation relation {γ_(v), γ_(μ)}=δ_(vμ)I.U_(S)=Σ_(μ)S_(v) ^(μ)γ_(μ), where S is a real orthogonal transformation.The group of Bogoliubov transformations are simply the real rotations ofthe 2N vectors γ_(i). This group contains, as a subgroup, the orbitalrotation group SU(N) that was considered previously. Let U_(S) be theunitary representation of a Majorana rotation and let S_(μ) ^(v) be thematrix entries of the corresponding rotation in

^(2N), then each Majorana operator transforms as U_(S)^(†)γ_(v)U_(S)=Σ_(μ)S_(v) ^(μ)γ_(μ). Such transformations alter theHamiltonian as follows. In terms of Majorana operators, the originalHamiltonian is

${H = {{\sum\limits_{v,\mu}{\Sigma_{v\; \mu}\gamma_{v}\gamma_{\mu}}} + {\sum\limits_{v,\mu,\alpha,\beta}{\Delta_{v\; \mu \; \alpha \; \beta}\gamma_{v}\gamma_{\mu}\gamma_{\alpha}\gamma_{\beta}}}}},$

where Σ_(vμ) and Δ_(vμαβ) are obtained from S_(vμ) and D_(vμαβ). Makingthe replacement, U_(S) ^(†)γ_(v)U_(S)=Σ_(μ)S_(v) ^(μ)γ_(μ), we write thetransformed Hamiltonian as

$\begin{matrix}{{H(S)} = {U_{S}^{\dagger}{HU}_{S}}} \\{= {{\sum\limits_{v,\mu}{\Sigma_{v\; \mu}{\sum\limits_{n,m}{S_{v}^{n}S_{\mu}^{m}\gamma_{n}\gamma_{m}}}}} +}} \\{{\sum\limits_{v,\mu,\alpha,\beta}{\sum\limits_{n,m,a,b}{\Delta_{v\; \mu \; \alpha \; \beta}S_{v}^{n}S_{\mu}^{m}S_{\alpha}^{a}S_{\beta}^{b}}}}} \\{{\gamma_{n}\gamma_{m}\gamma_{a}{\gamma_{b}.}}}\end{matrix}$

The marginals which may be computed for the marginals optimizationprocedure are of the form,

Λ_(nm) =Tr(γ_(n)γ_(m)ρ({right arrow over (θ*)}))

Ω_(nmab) =Tr(γ_(n)γ_(m)γ_(a)γ_(b)ρ({right arrow over (θ*)})).

Through either the Jordan-Wigner or Bravyi-Kitaev transformations, whichmap fermionic operators to qubit operators, the Majorana operators maybe mapped to Pauli product operators. In computing the reduced densitymatrices Tr(a_(i) ^(†)a_(j) ^(†)a_(k)a_(l)ρ({right arrow over (θ*)}))for a standard VQE problem, one may compute the expectations of thePauli product operators which comprise the creation and annihilationoperators. These Pauli product operators are precisely products of theMajorana operators. Consequently, in performing the marginalsoptimization procedure over Bogoliubov transformations, the relevantmarginals Λ_(nm) and Ω_(nmab), are simply those Pauli productexpectation values which have been already computed. In terms of theseMajorana operator marginals, we can write the Bogoliubov-transformedenergy expectation value as

H(S)

=Tr(ΣSΛS^(T))+Tr(Δ(S{circle around (×)}S)Ω(S ^(T) {circle around (×)}S^(T))),

where we have reshaped S,V and D,W into 2N-by-2N and (2N)²-by-(2N)²matrices, respectively. Finally, by carrying out the followingoptimization problem we can, in general, obtain an improved estimate ofthe ground state energy,

${\min\limits_{S}{{Tr}\left( {\Sigma \; S\; \Lambda \; S^{T}} \right)}} + {{Tr}\left( {{\Delta \left( {S \otimes S} \right)}{\Omega \left( {S^{T} \otimes S^{T}} \right)}} \right)}$subject  to  SS^(T) = I  and  S^(T)S = I

We can approach this optimization problem using the tools introducedabove for the case of orbital rotation. The essential difference is thatthe matrices have all real entries, leading to solving a realsemidefinite program.

Another common Hamiltonian model of interest is the spin Hamiltonian.This is used to describe the behavior of certain magnetic materials. Inthe case of spin-½ systems, the two-body Hamiltonian takes the generalform

${H = {{\sum\limits_{i,\mu}{S_{i,\mu}\sigma_{i}^{\mu}}} + {\sum\limits_{i,\mu}{D_{i,j,\mu,v}{\sigma_{i}^{\mu} \otimes \sigma_{j}^{v}}}}}},$

where σ_(i) ^(μ) of represent the X, Y, and Z, Pauli operators on theith qubit. While the orbital rotations preserve the form of theHamiltonian in the fermionic case, local unitary rotationsU_(R)=R₁{circle around (×)} . . . {circumflex over (×)}R_(N) preservethe form of the spin Hamiltonian,

$\begin{matrix}{{U_{R}^{\dagger}{HU}_{R}} = {{\sum\limits_{i,\mu}{S_{i,\mu}U_{R}^{\dagger}\sigma_{i}^{\mu}U_{R}}} + {\sum\limits_{i,\mu}{D_{i,j,\mu,v}U_{R}^{\dagger}{\sigma_{i}^{\mu} \otimes \sigma_{j}^{v}}U_{R}}}}} \\{= {{\sum\limits_{i,\mu}{S_{i,\mu}R_{i}^{\dagger}\sigma_{i}^{\mu}R_{i}}} +}} \\{{\sum\limits_{i,j,\mu,v}{D_{i,j,\mu,v}R_{i}^{\dagger}\sigma_{i}^{\mu}{R_{i} \otimes R_{j}^{\dagger}}\sigma_{j}^{v}{R_{j}.}}}}\end{matrix}$

Using the fact that the adjoint representation R{circle around (×)}R^(†)of the SU(2) transformations are SO(3) transformations O, we write

${{U_{R}^{\dagger}{HU}_{R}} = {{\sum\limits_{i,\mu,m}{S_{i,\mu}O_{m,i}^{\mu}\sigma_{i}^{m}}} + {\sum\limits_{i,j,\mu,v,m,n}{D_{i,j,\mu,v}O_{m,i}^{\mu}O_{n,j}^{v}{\sigma_{i}^{m} \otimes \sigma_{j}^{n}}}}}},$

where the operators O_(i) are determinant one, orthogonal,three-by-three matrices (i.e. SO(3)). Let L_(i) ^(m)=Tr(σ_(i)^(m)ρ({right arrow over (θ)})) and M_(ij) ^(mn)=Tr(σ_(i) ^(m){circlearound (×)}σ_(j) ^(n)ρ({right arrow over (θ)})) be the one- and two-bodyRDMs, respectively, of different Pauli products. Then thelocal-transformed energy expectation value is

$\begin{matrix}{{\langle{H(R)}\rangle} = {{\sum\limits_{i,\mu,m}{S_{i,\mu}O_{m,i}^{\mu}L_{i}^{m}}} +}} \\{{\sum\limits_{i,j,\mu,v,m,n}{D_{i,j,\mu,v}O_{m,i}^{\mu}O_{n,j}^{v}M_{ij}^{mn}}}} \\{= {{\sum\limits_{i}{{\overset{\rightarrow}{S}}_{i}^{T}O_{i}{\overset{\rightarrow}{L}}_{i}}} + {\sum\limits_{i,j}{{{Tr}\left( {D_{ij}O_{i}M_{ij}O_{j}^{T}} \right)}.}}}}\end{matrix}$

Many combinatorial optimization problems can be recast as an IsingHamiltonian energy minimization problem. The quantum approximateoptimization algorithm [?] was proposed for generating approximatesolutions to such optimization problems, in particular, focusing on thegraph-theoretic problem of MAXCUT. The Hamiltonian considered in theQAOA algorithm is a standard classical Ising Hamiltonian

${H = {\sum\limits_{{\langle{i,j}\rangle} \in E}{Z_{i} \otimes Z_{j}}}},$

where E is the set of edges of the graph defining the MAXCUT problem.This Hamiltonian is a specific instance of the spin Hamiltoniansconsidered above.

FIG. 4 is a flow chart of a quantum optimization method 400. Method 400may be performed on either a classical computer, or a hybridquantum-classical computer. Method 400 starts at a block 402. In a block404, an expectation value of a Hamiltonian is estimated for a quantumstate. The Hamiltonian is expressed as a linear combination ofobservables, and the Hamiltonian is estimated based on expectationvalues of the observables. In a block 412, one or both of theHamiltonian and the quantum state are transformed to reduce theexpectation value of the Hamiltonian. In one embodiment, the expectationvalue of the Hamiltonian is minimized in block 412. The minimization maybe implemented with semidefinite programming techniques.

In some embodiments, method 400 includes a block 406 in which theexpectation value of each of the observables is measured on a quantumcomputer. Block 406 may contain sub-blocks 408 and 410. In sub-block408, the quantum state is generated on the quantum computer. Insub-block 410, an observable is measured with the quantum state toobtain the expectation value of the observable. For any one observable,blocks 408 and 410 may be repeated to obtain sufficient statistics ofthe measurements to accurately determine the expectation value of theone observable. Blocks 408 and 410 may also be repeated for all theobservables so that all the expectation values are obtained viameasurements on the quantum computer.

In some embodiments, method 400 includes a block 414 in which theHamiltonian is updated based on the transforming in block 412. Block 414may be implemented on a classical computer. For example, in block 412 atransformation (e.g., a unitary transformation) may be identified that,when applied either to the Hamiltonian or the quantum state, lowers theestimated energy (i.e., the expectation value of the Hamiltonian). Inblock 414, the Hamiltonian is updated by applying the identifiedtransformation to the Hamiltonian to generate an updated Hamiltonian.The quantum state better approximates the ground state of the updatedHamiltonian, as compared to the Hamiltonian prior to updating.

In some embodiments, a parametrized quantum circuit, programmable viaone or more circuit parameters, is used in sub-block 408 to generate thequantum state. In some of these embodiments, method 400 further includesa block 416 in which the circuit parameters are updated so that theparametrized quantum circuit outputs an updated quantum state thatbetter approximates the ground state of the Hamiltonian (either beforeor after transforming in block 412). Block 416 may be implemented on aclassical computer. For example, a classical optimization algorithm maybe used to select the new circuit parameters to minimize a cost functionthat quantifies a distance between the quantum state and a target state(e.g., a ground state). The cost function may be based on theHamiltonian prior to updating in block 416, or on the updatedHamiltonian generated in block 416.

In some embodiments, method 400 includes a decision 418 that checks forconvergence of the circuit parameters. If, in decision 418, the circuitparameters are updated by an amount that is below a threshold, then thecircuit parameters have converged and method 400 ends at block 420. If,in decision 418, the circuit parameters are updated by an amount that isabove the threshold, then the circuit parameters have not converged andmethod 400 repeats blocks 406, 412, 414, and 416 to obtain a betterapproximation of the ground state and the corresponding ground-stateenergy. In sub-block 408, the parameterized quantum circuit receives theupdated circuit parameters determined in block 416 to generate theupdated quantum state. Blocks 406, 412, 414, and 416 may continue torepeat until it is determined in decision 418 that the circuitparameters have converged.

In other embodiments, block 406 (including sub-blocks 408 and 410) maybe implemented on a classical computer rather than a quantum computer.In these embodiments, all of method 400 is implemented on the classicalcomputer to simulate operation of the quantum computer. Morespecifically, in block 406, the expectation value of each of theobservables may be determined on the classical computer. For example,the expectation value may be calculated deterministically via anequation or deterministic model. Alternatively, the expectation valuemay be determined stochastically (e.g., to simulate the randomnessinherent to measurements performed on the quantum computer).

In some of the embodiments where all of method 400 is implemented on aclassical computer, the quantum state may be represented on theclassical computer as a first representation, in which case the firstrepresentation of the quantum state may be updated in block 416 insteadof the circuit parameters. Blocks 406, 412, 414, and 416 may then berepeated until the first representation of the quantum state converges.

In some embodiments of method 400, the linear combination of theobservables includes at least one observable with a zero weight thatbecomes non-zero when the Hamiltonian is transformed in block 412. Inthese embodiments, the expectation value of an observable with a zeroweight will not contribute to the expectation value of the Hamiltonian.However, after the Hamiltonian is transformed in block 414, the zeroweight for the observable may become non-zero, in which the expectationvalue of the observable will contribute to the expectation value of theHamiltonian. Thus, in these embodiments, the expectation values of theobservables include an expectation value for the at least one observablewith a zero weight.

In other embodiments of method 400, a fermionic transformation isapplied, in block 412, to one or both of the Hamiltonian and the quantumstate. In some of these embodiments, the fermionic transformationinclude rotations of active orbitals. The fermionic transformation mayinclude transformations out of an active space, of the active orbitals,to incorporate at least one of a core orbital and a virtual orbital. Thefermionic transformation may include rotations that respect one or moreof an open-shell spin symmetry, a closed-shell spin symmetry, and ageometric symmetry. In some of these embodiments, method 400 isimplemented with a quantum subspace expansion technique, or a marginalprojection technique, as described above. In other of these embodiments,the expectation values of the observables are obtained via orbitalframes.

In other embodiments of method 400, a Majorana fermionic transformationis applied, in block 412, to one or both of the Hamiltonian and thequantum state. In some of these embodiments, the expectation value ofthe Hamiltonian is minimized using a Givens parameterization. In otherof these embodiments, the expectation value of the Hamiltonian isminimized using semidefinite programming.

In other embodiments of method 400, a spin transformation is applied, inblock 412, to one or both of the Hamiltonian and the quantum state. Insome of these embodiments, the expectation value of the Hamiltonian isminimized using semidefinite programming.

In other embodiments of method 400, the Hamiltonian is an IsingHamiltonian configured for solving a combinatorial optimization problem.In some of these embodiments, the expectation value of the Hamiltonianis minimized using semidefinite programming.

It is to be understood that although the invention has been describedabove in terms of particular embodiments, the foregoing embodiments areprovided as illustrative only, and do not limit or define the scope ofthe invention. Various other embodiments, including but not limited tothe following, are also within the scope of the claims. For example,elements and components described herein may be further divided intoadditional components or joined together to form fewer components forperforming the same functions.

Various physical embodiments of a quantum computer are suitable for useaccording to the present disclosure. In general, the fundamental datastorage unit in quantum computing is the quantum bit, or qubit. Thequbit is a quantum-computing analog of a classical digital computersystem bit. A classical bit is considered to occupy, at any given pointin time, one of two possible states corresponding to the binary digits(bits) 0 or 1. By contrast, a qubit is implemented in hardware by aphysical medium with quantum-mechanical characteristics. Such a medium,which physically instantiates a qubit, may be referred to herein as a“physical instantiation of a qubit,” a “physical embodiment of a qubit,”a “medium embodying a qubit,” or similar terms, or simply as a “qubit,”for ease of explanation. It should be understood, therefore, thatreferences herein to “qubits” within descriptions of embodiments of thepresent invention refer to physical media which embody qubits.

Each qubit has an infinite number of different potentialquantum-mechanical states. When the state of a qubit is physicallymeasured, the measurement produces one of two different basis statesresolved from the state of the qubit. Thus, a single qubit can representa one, a zero, or any quantum superposition of those two qubit states; apair of qubits can be in any quantum superposition of 4 orthogonal basisstates; and three qubits can be in any superposition of 8 orthogonalbasis states. The function that defines the quantum-mechanical states ofa qubit is known as its wavefunction. The wavefunction also specifiesthe probability distribution of outcomes for a given measurement. Aqubit, which has a quantum state of dimension two (i.e., has twoorthogonal basis states), may be generalized to a d-dimensional “qudit,”where d may be any integral value, such as 2, 3, 4, or higher. In thegeneral case of a qudit, measurement of the qudit produces one of ddifferent basis states resolved from the state of the qudit. Anyreference herein to a qubit should be understood to refer more generallyto an d-dimensional qudit with any value of d.

Although certain descriptions of qubits herein may describe such qubitsin terms of their mathematical properties, each such qubit may beimplemented in a physical medium in any of a variety of different ways.Examples of such physical media include superconducting material,trapped ions, photons, optical cavities, individual electrons trappedwithin quantum dots, point defects in solids (e.g., phosphorus donors insilicon or nitrogen-vacancy centers in diamond), molecules (e.g.,alanine, vanadium complexes), or aggregations of any of the foregoingthat exhibit qubit behavior, that is, comprising quantum states andtransitions therebetween that can be controllably induced or detected.

For any given medium that implements a qubit, any of a variety ofproperties of that medium may be chosen to implement the qubit. Forexample, if electrons are chosen to implement qubits, then the xcomponent of its spin degree of freedom may be chosen as the property ofsuch electrons to represent the states of such qubits. Alternatively,the y component, or the z component of the spin degree of freedom may bechosen as the property of such electrons to represent the state of suchqubits. This is merely a specific example of the general feature thatfor any physical medium that is chosen to implement qubits, there may bemultiple physical degrees of freedom (e.g., the x, y, and z componentsin the electron spin example) that may be chosen to represent 0 and 1.For any particular degree of freedom, the physical medium maycontrollably be put in a state of superposition, and measurements maythen be taken in the chosen degree of freedom to obtain readouts ofqubit values.

Certain implementations of quantum computers, referred as gate modelquantum computers, comprise quantum gates. In contrast to classicalgates, there is an infinite number of possible single-qubit quantumgates that change the state vector of a qubit. Changing the state of aqubit state vector typically is referred to as a single-qubit rotation,and may also be referred to herein as a state change or a single-qubitquantum-gate operation. A rotation, state change, or single-qubitquantum-gate operation may be represented mathematically by a unitary2×2 matrix with complex elements. A rotation corresponds to a rotationof a qubit state within its Hilbert space, which may be conceptualizedas a rotation of the Bloch sphere. (As is well-known to those havingordinary skill in the art, the Bloch sphere is a geometricalrepresentation of the space of pure states of a qubit.) Multi-qubitgates alter the quantum state of a set of qubits. For example, two-qubitgates rotate the state of two qubits as a rotation in thefour-dimensional Hilbert space of the two qubits. (As is well-known tothose having ordinary skill in the art, a Hilbert space is an abstractvector space possessing the structure of an inner product that allowslength and angle to be measured. Furthermore, Hilbert spaces arecomplete: there are enough limits in the space to allow the techniquesof calculus to be used.)

A quantum circuit may be specified as a sequence of quantum gates. Asdescribed in more detail below, the term “quantum gate,” as used herein,refers to the application of a gate control signal (defined below) toone or more qubits to cause those qubits to undergo certain physicaltransformations and thereby to implement a logical gate operation. Toconceptualize a quantum circuit, the matrices corresponding to thecomponent quantum gates may be multiplied together in the orderspecified by the gate sequence to produce a 2^(n)×2^(n) complex matrixrepresenting the same overall state change on n qubits. A quantumcircuit may thus be expressed as a single resultant operator. However,designing a quantum circuit in terms of constituent gates allows thedesign to conform to a standard set of gates, and thus enable greaterease of deployment. A quantum circuit thus corresponds to a design foractions taken upon the physical components of a quantum computer.

A given variational quantum circuit may be parameterized in a suitabledevice-specific manner. More generally, the quantum gates making up aquantum circuit may have an associated plurality of tuning parameters.For example, in embodiments based on optical switching, tuningparameters may correspond to the angles of individual optical elements.

In certain embodiments of quantum circuits, the quantum circuit includesboth one or more gates and one or more measurement operations. Quantumcomputers implemented using such quantum circuits are referred to hereinas implementing “measurement feedback.” For example, a quantum computerimplementing measurement feedback may execute the gates in a quantumcircuit and then measure only a subset (i.e., fewer than all) of thequbits in the quantum computer, and then decide which gate(s) to executenext based on the outcome(s) of the measurement(s). In particular, themeasurement(s) may indicate a degree of error in the gate operation(s),and the quantum computer may decide which gate(s) to execute next basedon the degree of error. The quantum computer may then execute thegate(s) indicated by the decision. This process of executing gates,measuring a subset of the qubits, and then deciding which gate(s) toexecute next may be repeated any number of times. Measurement feedbackmay be useful for performing quantum error correction, but is notlimited to use in performing quantum error correction. For every quantumcircuit, there is an error-corrected implementation of the circuit withor without measurement feedback.

Some embodiments described herein generate, measure, or utilize quantumstates that approximate a target quantum state (e.g., a ground state ofa Hamiltonian). As will be appreciated by those trained in the art,there are many ways to quantify how well a first quantum state“approximates” a second quantum state. In the following description, anyconcept or definition of approximation known in the art may be usedwithout departing from the scope hereof. For example, when the first andsecond quantum states are represented as first and second vectors,respectively, the first quantum state approximates the second quantumstate when an inner product between the first and second vectors (calledthe “fidelity” between the two quantum states) is greater than apredefined amount (typically labeled E). In this example, the fidelityquantifies how “close” or “similar” the first and second quantum statesare to each other. The fidelity represents a probability that ameasurement of the first quantum state will give the same result as ifthe measurement were performed on the second quantum state. Proximitybetween quantum states can also be quantified with a distance measure,such as a Euclidean norm, a Hamming distance, or another type of normknown in the art. Proximity between quantum states can also be definedin computational terms. For example, the first quantum stateapproximates the second quantum state when a polynomial time-sampling ofthe first quantum state gives some desired information or property thatit shares with the second quantum state.

Not all quantum computers are gate model quantum computers. Embodimentsof the present invention are not limited to being implemented using gatemodel quantum computers. As an alternative example, embodiments of thepresent invention may be implemented, in whole or in part, using aquantum computer that is implemented using a quantum annealingarchitecture, which is an alternative to the gate model quantumcomputing architecture. More specifically, quantum annealing (QA) is ametaheuristic for finding the global minimum of a given objectivefunction over a given set of candidate solutions (candidate states), bya process using quantum fluctuations.

FIG. 2B shows a diagram illustrating operations typically performed by acomputer system 250 which implements quantum annealing. The system 250includes both a quantum computer 252 and a classical computer 254.Operations shown on the left of the dashed vertical line 256 typicallyare performed by the quantum computer 252, while operations shown on theright of the dashed vertical line 256 typically are performed by theclassical computer 254.

Quantum annealing starts with the classical computer 254 generating aninitial Hamiltonian 260 and a final Hamiltonian 262 based on acomputational problem 258 to be solved, and providing the initialHamiltonian 260, the final Hamiltonian 262 and an annealing schedule 270as input to the quantum computer 252. The quantum computer 252 preparesa well-known initial state 266 (FIG. 2B, operation 264), such as aquantum-mechanical superposition of all possible states (candidatestates) with equal weights, based on the initial Hamiltonian 260. Theclassical computer 254 provides the initial Hamiltonian 260, a finalHamiltonian 262, and an annealing schedule 270 to the quantum computer252. The quantum computer 252 starts in the initial state 266, andevolves its state according to the annealing schedule 270 following thetime-dependent Schrödinger equation, a natural quantum-mechanicalevolution of physical systems (FIG. 2B, operation 268). Morespecifically, the state of the quantum computer 252 undergoes timeevolution under a time-dependent Hamiltonian, which starts from theinitial Hamiltonian 260 and terminates at the final Hamiltonian 262. Ifthe rate of change of the system Hamiltonian is slow enough, the systemstays close to the ground state of the instantaneous Hamiltonian. If therate of change of the system Hamiltonian is accelerated, the system mayleave the ground state temporarily but produce a higher likelihood ofconcluding in the ground state of the final problem Hamiltonian, i.e.,diabatic quantum computation. At the end of the time evolution, the setof qubits on the quantum annealer is in a final state 272, which isexpected to be close to the ground state of the classical Ising modelthat corresponds to the solution to the original optimization problem258. An experimental demonstration of the success of quantum annealingfor random magnets was reported immediately after the initialtheoretical proposal.

The final state 272 of the quantum computer 254 is measured, therebyproducing results 276 (i.e., measurements) (FIG. 2B, operation 274). Themeasurement operation 274 may be performed, for example, in any of theways disclosed herein, such as in any of the ways disclosed herein inconnection with the measurement unit 110 in FIG. 1. The classicalcomputer 254 performs postprocessing on the measurement results 276 toproduce output 280 representing a solution to the original computationalproblem 258 (FIG. 2B, operation 278).

As yet another alternative example, embodiments of the present inventionmay be implemented, in whole or in part, using a quantum computer thatis implemented using a one-way quantum computing architecture, alsoreferred to as a measurement-based quantum computing architecture, whichis another alternative to the gate model quantum computing architecture.More specifically, the one-way or measurement based quantum computer(MBQC) is a method of quantum computing that first prepares an entangledresource state, usually a cluster state or graph state, then performssingle qubit measurements on it. It is “one-way” because the resourcestate is destroyed by the measurements.

The outcome of each individual measurement is random, but they arerelated in such a way that the computation always succeeds. In general,the choices of basis for later measurements need to depend on theresults of earlier measurements, and hence the measurements cannot allbe performed at the same time.

Any of the functions disclosed herein may be implemented using means forperforming those functions. Such means include, but are not limited to,any of the components disclosed herein, such as the computer-relatedcomponents described below.

Referring to FIG. 1, a diagram is shown of a system 100 implementedaccording to one embodiment of the present invention. Referring to FIG.2A, a flowchart is shown of a method 200 performed by the system 100 ofFIG. 1 according to one embodiment of the present invention. The system100 includes a quantum computer 102. The quantum computer 102 includes aplurality of qubits 104, which may be implemented in any of the waysdisclosed herein. There may be any number of qubits 104 in the quantumcomputer 104. For example, the qubits 104 may include or consist of nomore than 2 qubits, no more than 4 qubits, no more than 8 qubits, nomore than 16 qubits, no more than 32 qubits, no more than 64 qubits, nomore than 128 qubits, no more than 256 qubits, no more than 512 qubits,no more than 1024 qubits, no more than 2048 qubits, no more than 4096qubits, or no more than 8192 qubits. These are merely examples, inpractice there may be any number of qubits 104 in the quantum computer102.

There may be any number of gates in a quantum circuit. However, in someembodiments the number of gates may be at least proportional to thenumber of qubits 104 in the quantum computer 102. In some embodimentsthe gate depth may be no greater than the number of qubits 104 in thequantum computer 102, or no greater than some linear multiple of thenumber of qubits 104 in the quantum computer 102 (e.g., 2, 3, 4, 5, 6,or 7).

The qubits 104 may be interconnected in any graph pattern. For example,they be connected in a linear chain, a two-dimensional grid, anall-to-all connection, any combination thereof, or any subgraph of anyof the preceding.

As will become clear from the description below, although element 102 isreferred to herein as a “quantum computer,” this does not imply that allcomponents of the quantum computer 102 leverage quantum phenomena. Oneor more components of the quantum computer 102 may, for example, beclassical (i.e., non-quantum components) components which do notleverage quantum phenomena.

The quantum computer 102 includes a control unit 106, which may includeany of a variety of circuitry and/or other machinery for performing thefunctions disclosed herein. The control unit 106 may, for example,consist entirely of classical components. The control unit 106 generatesand provides as output one or more control signals 108 to the qubits104. The control signals 108 may take any of a variety of forms, such asany kind of electromagnetic signals, such as electrical signals,magnetic signals, optical signals (e.g., laser pulses), or anycombination thereof.

For example:

-   -   In embodiments in which some or all of the qubits 104 are        implemented as photons (also referred to as a “quantum optical”        implementation) that travel along waveguides, the control unit        106 may be a beam splitter (e.g., a heater or a mirror), the        control signals 108 may be signals that control the heater or        the rotation of the mirror, the measurement unit 110 may be a        photodetector, and the measurement signals 112 may be photons.    -   In embodiments in which some or all of the qubits 104 are        implemented as charge type qubits (e.g., transmon, X-mon, G-mon)        or flux-type qubits (e.g., flux qubits, capacitively shunted        flux qubits) (also referred to as a “circuit quantum        electrodynamic” (circuit QED) implementation), the control unit        106 may be a bus resonator activated by a drive, the control        signals 108 may be cavity modes, the measurement unit 110 may be        a second resonator (e.g., a low-Q resonator), and the        measurement signals 112 may be voltages measured from the second        resonator using dispersive readout techniques.    -   In embodiments in which some or all of the qubits 104 are        implemented as superconducting circuits, the control unit 106        may be a circuit QED-assisted control unit or a direct        capacitive coupling control unit or an inductive capacitive        coupling control unit, the control signals 108 may be cavity        modes, the measurement unit 110 may be a second resonator (e.g.,        a low-Q resonator), and the measurement signals 112 may be        voltages measured from the second resonator using dispersive        readout techniques.    -   In embodiments in which some or all of the qubits 104 are        implemented as trapped ions (e.g., electronic states of, e.g.,        magnesium ions), the control unit 106 may be a laser, the        control signals 108 may be laser pulses, the measurement unit        110 may be a laser and either a CCD or a photodetector (e.g., a        photomultiplier tube), and the measurement signals 112 may be        photons.    -   In embodiments in which some or all of the qubits 104 are        implemented using nuclear magnetic resonance (NMR) (in which        case the qubits may be molecules, e.g., in liquid or solid        form), the control unit 106 may be a radio frequency (RF)        antenna, the control signals 108 may be RF fields emitted by the        RF antenna, the measurement unit 110 may be another RF antenna,        and the measurement signals 112 may be RF fields measured by the        second RF antenna.    -   In embodiments in which some or all of the qubits 104 are        implemented as nitrogen-vacancy centers (NV centers), the        control unit 106 may, for example, be a laser, a microwave        antenna, or a coil, the control signals 108 may be visible        light, a microwave signal, or a constant electromagnetic field,        the measurement unit 110 may be a photodetector, and the        measurement signals 112 may be photons.    -   In embodiments in which some or all of the qubits 104 are        implemented as two-dimensional quasiparticles called “anyons”        (also referred to as a “topological quantum computer”        implementation), the control unit 106 may be nanowires, the        control signals 108 may be local electrical fields or microwave        pulses, the measurement unit 110 may be superconducting        circuits, and the measurement signals 112 may be voltages.    -   In embodiments in which some or all of the qubits 104 are        implemented as semiconducting material (e.g., nanowires), the        control unit 106 may be microfabricated gates, the control        signals 108 may be RF or microwave signals, the measurement unit        110 may be microfabricated gates, and the measurement signals        112 may be RF or microwave signals.

Although not shown explicitly in FIG. 1 and not required, themeasurement unit 110 may provide one or more feedback signals 114 to thecontrol unit 106 based on the measurement signals 112. For example,quantum computers referred to as “one-way quantum computers” or“measurement-based quantum computers” utilize such feedback 114 from themeasurement unit 110 to the control unit 106. Such feedback 114 is alsonecessary for the operation of fault-tolerant quantum computing anderror correction.

The control signals 108 may, for example, include one or more statepreparation signals which, when received by the qubits 104, cause someor all of the qubits 104 to change their states. Such state preparationsignals constitute a quantum circuit also referred to as an “ansatzcircuit.” The resulting state of the qubits 104 is referred to herein asan “initial state” or an “ansatz state.” The process of outputting thestate preparation signal(s) to cause the qubits 104 to be in theirinitial state is referred to herein as “state preparation” (FIG. 2A,section 206). A special case of state preparation is “initialization,”also referred to as a “reset operation,” in which the initial state isone in which some or all of the qubits 104 are in the “zero” state i.e.the default single-qubit state. More generally, state preparation mayinvolve using the state preparation signals to cause some or all of thequbits 104 to be in any distribution of desired states. In someembodiments, the control unit 106 may first perform initialization onthe qubits 104 and then perform preparation on the qubits 104, by firstoutputting a first set of state preparation signals to initialize thequbits 104, and by then outputting a second set of state preparationsignals to put the qubits 104 partially or entirely into non-zerostates.

Another example of control signals 108 that may be output by the controlunit 106 and received by the qubits 104 are gate control signals. Thecontrol unit 106 may output such gate control signals, thereby applyingone or more gates to the qubits 104. Applying a gate to one or morequbits causes the set of qubits to undergo a physical state change whichembodies a corresponding logical gate operation (e.g., single-qubitrotation, two-qubit entangling gate or multi-qubit operation) specifiedby the received gate control signal. As this implies, in response toreceiving the gate control signals, the qubits 104 undergo physicaltransformations which cause the qubits 104 to change state in such a waythat the states of the qubits 104, when measured (see below), representthe results of performing logical gate operations specified by the gatecontrol signals. The term “quantum gate,” as used herein, refers to theapplication of a gate control signal to one or more qubits to causethose qubits to undergo the physical transformations described above andthereby to implement a logical gate operation.

It should be understood that the dividing line between state preparation(and the corresponding state preparation signals) and the application ofgates (and the corresponding gate control signals) may be chosenarbitrarily. For example, some or all the components and operations thatare illustrated in FIGS. W and X as elements of “state preparation” mayinstead be characterized as elements of gate application. Conversely,for example, some or all of the components and operations that areillustrated in FIGS. W and X as elements of “gate application” mayinstead be characterized as elements of state preparation. As oneparticular example, the system and method of FIGS. W and X may becharacterized as solely performing state preparation followed bymeasurement, without any gate application, where the elements that aredescribed herein as being part of gate application are insteadconsidered to be part of state preparation. Conversely, for example, thesystem and method of FIGS. W and X may be characterized as solelyperforming gate application followed by measurement, without any statepreparation, and where the elements that are described herein as beingpart of state preparation are instead considered to be part of gateapplication.

The quantum computer 102 also includes a measurement unit 110, whichperforms one or more measurement operations on the qubits 104 to readout measurement signals 112 (also referred to herein as “measurementresults”) from the qubits 104, where the measurement results 112 aresignals representing the states of some or all of the qubits 104. Inpractice, the control unit 106 and the measurement unit 110 may beentirely distinct from each other, or contain some components in commonwith each other, or be implemented using a single unit (i.e., a singleunit may implement both the control unit 106 and the measurement unit110). For example, a laser unit may be used both to generate the controlsignals 108 and to provide stimulus (e.g., one or more laser beams) tothe qubits 104 to cause the measurement signals 112 to be generated.

In general, the quantum computer 102 may perform various operationsdescribed above any number of times. For example, the control unit 106may generate one or more control signals 108, thereby causing the qubits104 to perform one or more quantum gate operations. The measurement unit110 may then perform one or more measurement operations on the qubits104 to read out a set of one or more measurement signals 112. Themeasurement unit 110 may repeat such measurement operations on thequbits 104 before the control unit 106 generates additional controlsignals 108, thereby causing the measurement unit 110 to read outadditional measurement signals 112 resulting from the same gateoperations that were performed before reading out the previousmeasurement signals 112. The measurement unit 110 may repeat thisprocess any number of times to generate any number of measurementsignals 112 corresponding to the same gate operations. The quantumcomputer 102 may then aggregate such multiple measurements of the samegate operations in any of a variety of ways.

After the measurement unit 110 has performed one or more measurementoperations on the qubits 104 after they have performed one set of gateoperations, the control unit 106 may generate one or more additionalcontrol signals 108, which may differ from the previous control signals108, thereby causing the qubits 104 to perform one or more additionalquantum gate operations, which may differ from the previous set ofquantum gate operations. The process described above may then berepeated, with the measurement unit 110 performing one or moremeasurement operations on the qubits 104 in their new states (resultingfrom the most recently-performed gate operations).

In general, the system 100 may implement a plurality of quantum circuitsas follows. For each quantum circuit C in the plurality of quantumcircuits (FIG. 2A, operation 202), the system 100 performs a pluralityof “shots” on the qubits 104. The meaning of a shot will become clearfrom the description that follows. For each shot S in the plurality ofshots (FIG. 2A, operation 204), the system 100 prepares the state of thequbits 104 (FIG. 2A, section 206). More specifically, for each quantumgate G in quantum circuit C (FIG. 2A, operation 210), the system 100applies quantum gate G to the qubits 104 (FIG. 2A, operations 212 and214).

Then, for each of the qubits Q 104 (FIG. 2A, operation 216), the system100 measures the qubit Q to produce measurement output representing acurrent state of qubit Q (FIG. 2A, operations 218 and 220).

The operations described above are repeated for each shot S (FIG. 2A,operation 222), and circuit C (FIG. 2A, operation 224). As thedescription above implies, a single “shot” involves preparing the stateof the qubits 104 and applying all of the quantum gates in a circuit tothe qubits 104 and then measuring the states of the qubits 104; and thesystem 100 may perform multiple shots for one or more circuits.

Referring to FIG. 3, a diagram is shown of a hybrid classical quantumcomputer (HQC) 300 implemented according to one embodiment of thepresent invention. The HQC 300 includes a quantum computer component 102(which may, for example, be implemented in the manner shown anddescribed in connection with FIG. 1) and a classical computer component306. The classical computer component may be a machine implementedaccording to the general computing model established by John VonNeumann, in which programs are written in the form of ordered lists ofinstructions and stored within a classical (e.g., digital) memory 310and executed by a classical (e.g., digital) processor 308 of theclassical computer. The memory 310 is classical in the sense that itstores data in a storage medium in the form of bits, which have a singledefinite binary state at any point in time. The bits stored in thememory 310 may, for example, represent a computer program. The classicalcomputer component 304 typically includes a bus 314. The processor 308may read bits from and write bits to the memory 310 over the bus 314.For example, the processor 308 may read instructions from the computerprogram in the memory 310, and may optionally receive input data 316from a source external to the computer 302, such as from a user inputdevice such as a mouse, keyboard, or any other input device. Theprocessor 308 may use instructions that have been read from the memory310 to perform computations on data read from the memory 310 and/or theinput 316, and generate output from those instructions. The processor308 may store that output back into the memory 310 and/or provide theoutput externally as output data 318 via an output device, such as amonitor, speaker, or network device.

The quantum computer component 102 may include a plurality of qubits104, as described above in connection with FIG. 1. A single qubit mayrepresent a one, a zero, or any quantum superposition of those two qubitstates. The classical computer component 304 may provide classical statepreparation signals Y32 to the quantum computer 102, in response towhich the quantum computer 102 may prepare the states of the qubits 104in any of the ways disclosed herein, such as in any of the waysdisclosed in connection with FIGS. 1 and 2A-2B.

Once the qubits 104 have been prepared, the classical processor 308 mayprovide classical control signals 334 to the quantum computer 102, inresponse to which the quantum computer 102 may apply the gate operationsspecified by the control signals 332 to the qubits 104, as a result ofwhich the qubits 104 arrive at a final state. The measurement unit 110in the quantum computer 102 (which may be implemented as described abovein connection with FIGS. 1 and 2A-2B) may measure the states of thequbits 104 and produce measurement output 338 representing the collapseof the states of the qubits 104 into one of their eigenstates. As aresult, the measurement output 338 includes or consists of bits andtherefore represents a classical state. The quantum computer 102provides the measurement output 338 to the classical processor 308. Theclassical processor 308 may store data representing the measurementoutput 338 and/or data derived therefrom in the classical memory 310.

The steps described above may be repeated any number of times, with whatis described above as the final state of the qubits 104 serving as theinitial state of the next iteration. In this way, the classical computer304 and the quantum computer 102 may cooperate as co-processors toperform joint computations as a single computer system.

Although certain functions may be described herein as being performed bya classical computer and other functions may be described herein asbeing performed by a quantum computer, these are merely examples and donot constitute limitations of the present invention. A subset of thefunctions which are disclosed herein as being performed by a quantumcomputer may instead be performed by a classical computer. For example,a classical computer may execute functionality for emulating a quantumcomputer and provide a subset of the functionality described herein,albeit with functionality limited by the exponential scaling of thesimulation. Functions which are disclosed herein as being performed by aclassical computer may instead be performed by a quantum computer.

The techniques described above may be implemented, for example, inhardware, in one or more computer programs tangibly stored on one ormore computer-readable media, firmware, or any combination thereof, suchas solely on a quantum computer, solely on a classical computer, or on ahybrid classical quantum (HQC) computer. The techniques disclosed hereinmay, for example, be implemented solely on a classical computer, inwhich the classical computer emulates the quantum computer functionsdisclosed herein.

The techniques described above may be implemented in one or morecomputer programs executing on (or executable by) a programmablecomputer (such as a classical computer, a quantum computer, or an HQC)including any combination of any number of the following: a processor, astorage medium readable and/or writable by the processor (including, forexample, volatile and non-volatile memory and/or storage elements), aninput device, and an output device. Program code may be applied to inputentered using the input device to perform the functions described and togenerate output using the output device.

Embodiments of the present invention include features which are onlypossible and/or feasible to implement with the use of one or morecomputers, computer processors, and/or other elements of a computersystem. Such features are either impossible or impractical to implementmentally and/or manually. For example, in any practical use ofembodiments of the present invention, carrying out the optimization ofthe energy expectation value will be computationally demanding andimpossible to perform manually, or mentally. Even with a conservativeestimate, millions of individual computational steps would be needed.Even solely using a classical computer, the routine is, in general,likely inefficient because generating a variety of valid marginal datais challenging due to the QMA-completeness of the quantum marginalproblem.

Any claims herein which affirmatively require a computer, a processor, amemory, or similar computer-related elements, are intended to requiresuch elements, and should not be interpreted as if such elements are notpresent in or required by such claims. Such claims are not intended, andshould not be interpreted, to cover methods and/or systems which lackthe recited computer-related elements. For example, any method claimherein which recites that the claimed method is performed by a computer,a processor, a memory, and/or similar computer-related element, isintended to, and should only be interpreted to, encompass methods whichare performed by the recited computer-related element(s). Such a methodclaim should not be interpreted, for example, to encompass a method thatis performed mentally or by hand (e.g., using pencil and paper).Similarly, any product claim herein which recites that the claimedproduct includes a computer, a processor, a memory, and/or similarcomputer-related element, is intended to, and should only be interpretedto, encompass products which include the recited computer-relatedelement(s). Such a product claim should not be interpreted, for example,to encompass a product that does not include the recitedcomputer-related element(s).

In embodiments in which a classical computing component executes acomputer program providing any subset of the functionality within thescope of the claims below, the computer program may be implemented inany programming language, such as assembly language, machine language, ahigh-level procedural programming language, or an object-orientedprogramming language. The programming language may, for example, be acompiled or interpreted programming language.

Each such computer program may be implemented in a computer programproduct tangibly embodied in a machine-readable storage device forexecution by a computer processor, which may be either a classicalprocessor or a quantum processor. Method steps of the invention may beperformed by one or more computer processors executing a programtangibly embodied on a computer-readable medium to perform functions ofthe invention by operating on input and generating output. Suitableprocessors include, by way of example, both general and special purposemicroprocessors. Generally, the processor receives (reads) instructionsand data from a memory (such as a read-only memory and/or a randomaccess memory) and writes (stores) instructions and data to the memory.Storage devices suitable for tangibly embodying computer programinstructions and data include, for example, all forms of non-volatilememory, such as semiconductor memory devices, including EPROM, EEPROM,and flash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROMs. Any of theforegoing may be supplemented by, or incorporated in, specially-designedASICs (application-specific integrated circuits) or FPGAs(Field-Programmable Gate Arrays). A classical computer can generallyalso receive (read) programs and data from, and write (store) programsand data to, a non-transitory computer-readable storage medium such asan internal disk (not shown) or a removable disk. These elements willalso be found in a conventional desktop or workstation computer as wellas other computers suitable for executing computer programs implementingthe methods described herein, which may be used in conjunction with anydigital print engine or marking engine, display monitor, or other rasteroutput device capable of producing color or gray scale pixels on paper,film, display screen, or other output medium.

Any data disclosed herein may be implemented, for example, in one ormore data structures tangibly stored on a non-transitorycomputer-readable medium (such as a classical computer-readable medium,a quantum computer-readable medium, or an HQC computer-readable medium).Embodiments of the invention may store such data in such datastructure(s) and read such data from such data structure(s).

What is claimed is:
 1. A quantum optimization method, comprising:estimating, on a classical computer and for a quantum state, anexpectation value of a Hamiltonian, expressible as a linear combinationof observables, based on expectation values of the observables; andtransforming, on the classical computer, one or both of the Hamiltonianand the quantum state to reduce the expectation value of theHamiltonian.
 2. The quantum optimization method of claim 1, furthercomprising measuring the expectation value of each of the observables ona quantum computer by: generating the quantum state on the quantumcomputer; and measuring, on the quantum computer, said each of theobservables for the quantum state.
 3. The quantum optimization method ofclaim 2, wherein said generating the quantum state includes generatingthe quantum state with a parametrized quantum circuit programmable viaone or more circuit parameters.
 4. The quantum optimization method ofclaim 3, further comprising updating the one or more circuit parameterssuch that the parametrized quantum circuit outputs an updated quantumstate that better approximates a ground state of the Hamiltonian.
 5. Thequantum optimization method of claim 4, further comprising repeating:said generating the quantum state with the parametrized quantum circuit;said measuring each of the observables for the quantum state; saidtransforming one or both of the Hamiltonian and the quantum state;updating the Hamiltonian based on said transforming; and said updatingthe one or more circuit parameters; until the one or more circuitparameters have converged.
 6. The quantum optimization method of claim1, wherein said transforming one or both of the Hamiltonian and thequantum state includes applying a unitary transformation to said one orboth of the Hamiltonian and the quantum state.
 7. The quantumoptimization method of claim 1, further comprising generating, on theclassical computer, the expectation value of each of the observables. 8.The quantum optimization method of claim 7, further comprising updating,on the classical computer, a first representation of the quantum statebased on the expectation value of the Hamiltonian to better approximatea ground state of the Hamiltonian.
 9. The quantum optimization method ofclaim 8, further comprising repeating: said generating the expectationvalue of each of the observables; said transforming one or both of theHamiltonian and the quantum state; and said updating the firstrepresentation of the quantum state; until the first representation ofthe quantum state has converged.
 10. The quantum optimization method ofclaim 1, wherein: the linear combination of the observables includes atleast one observable with a zero weight that becomes non-zero due tosaid transforming the Hamiltonian; and the expectation values of theobservables include an expectation value for the at least one observablewith a zero weight.
 11. The quantum optimization method of claim 1,wherein said transforming one or both of the Hamiltonian and the quantumstate includes applying a fermionic transformation to said one or bothof the Hamiltonian and the quantum state.
 12. The quantum optimizationmethod of claim 11, the fermionic transformation including rotations ofactive orbitals.
 13. The quantum optimization method of claim 11, thefermionic transformation including transformations out of an activespace to incorporate at least one of a core orbital and a virtualorbital.
 14. The quantum optimization method of claim 11, the fermionictransformation including rotations that respect one or more of anopen-shell spin symmetry, a closed-shell spin symmetry, and a geometricsymmetry.
 15. The quantum optimization method of claim 11, furthercomprising implementing a quantum subspace expansion technique.
 16. Thequantum optimization method of claim 11, further comprising implementinga marginal projection technique.
 17. The quantum optimization method ofclaim 11, further comprising obtaining any of the expectation values theobservables via orbital frames.
 18. The quantum optimization method ofclaim 1, wherein said transforming one or both of the Hamiltonian andthe quantum state includes applying a Majorana fermionic transformationto said one or both of the Hamiltonian and the quantum state.
 19. Thequantum optimization method of claim 18, further comprising minimizingthe expectation value of the Hamiltonian using a Givensparameterization.
 20. The quantum optimization method of claim 18,further comprising minimizing the expectation value of the Hamiltonianusing semidefinite programming.
 21. The quantum optimization method ofclaim 1, wherein said transforming one or both of the Hamiltonian andthe quantum state includes applying a spin transformation to said one orboth of the Hamiltonian and the quantum state.
 22. The quantumoptimization method of claim 1, wherein the Hamiltonian is an IsingHamiltonian configured for solving a combinatorial optimization problem.23. The quantum optimization method of claim 1, wherein saidtransforming one or both of the Hamiltonian and the quantum stateincludes minimizing the expectation value of the Hamiltonian estimatedfor the quantum state.
 24. The quantum optimization method of claim 23,wherein said minimizing the expectation value of the Hamiltonianincludes minimizing the expectation value of the Hamiltonian usingsemidefinite programming.
 25. A computing system configured for quantumoptimization, comprising: a processor; a memory communicably coupledwith the processor and storing machine-readable instructions that, whenexecuted by the processor, control the computing system to: estimate,for a quantum state, an expectation value of a Hamiltonian, expressibleas a linear combination of observables, based on expectation values ofthe observables, and transform one or both of the Hamiltonian and thequantum state to reduce the expectation value of the Hamiltonianestimated for the quantum state.
 26. The computing system of claim 25,further comprising a quantum computer that is communicably coupled withthe processor and configured to measure the expectation value of each ofthe observables.